Publishing Your Python Library on PyPI

I explain how to publish Python library on PyPI with Poetry. I believe this video will be useful to all Python developers, who are looking at how to create Python library and publish it. I share my experience and explain why I spent the entire day debugging the issue with library dependencies.

 

ML Pipeline End-to-End Solution

Are you interested to learn how to build and run a complete ML pipeline - Web API, data processing, model training, and prediction services? In this video, I explain how the end-to-end solution works using our open-source product Skipper.

 

FastAPI Endpoint Type with Pydantic

Pydantic library helps to define structured and clear types for FastAPI endpoints. In this video I explain how to define Pydantic type with nested list structure, I show how it works with a live demo. You will get to know how to convert input data into JSON structure. Enjoy!

 

Event-Driven Microservice with RabbitMQ and FastAPI

Event-driven microservices architecture brings scalability and better application structure. In this video, I show a demo based on Web API implementation with FastAPI, Celery. The event is sent to a group of microservices through RabbitMQ broker. Services communicate with each other through RabbitMQ. Model training service calls data service to fetch and prepare data for training.

 

Web API with FastAPI, RabbitMQ and Celery

With Web API you can create access to microservice functionality. In this video, I explain how to create scalable Web API with FastAPI, Celery and RabbitMQ. Celery is responsible to execute async Web API requests and RabbitMQ enables the communication between Web API and microservices.

 

Celery Distributed Task Queue Display with Flower UI

I explain how you can visualize Celery distributed task queue with Flower UI. This is useful when monitoring asynchronous tasks.

 

Celery Distributed Task Queue with FastAPI for Machine Learning

This sample app demonstrates how to implement Celery distributed task queues on top of RabbitMQ broker for Machine Learning model training and data processing. We are using TensorFlow in this example to train the model. API request comes through FastAPI and it is being processed asynchronously by Celery. There is a separate API endpoint to check task status. Multiple requests can be initiated and processed at the same time in parallel. Celery tasks can be monitored using Flower monitoring tool.